Reference to background documents indicated by the square brackets [ ] refer to the “List of References” provided below.
With the ever-increasing demand for efficiency and flexibility, many engineering intensive applications opt to use multi-composed systems to conduct cooperative tasks. Such applications are widely spread across industry sectors, e.g., the multi-robot systems in automotive industry, the multi-actuated platforms in manufacturing, and formation flying of multiple flight vehicles or spacecraft (satellites) in aerospace, etc. The cooperative behavior for these multiple dynamic systems provides flexibility and maneuverability that cannot be achieved by an individual system. One key element to the success of such coordination is the motion synchronization among these involved components or systems. Motion synchronization addresses the cooperative or coordinated schemes of multi-composed systems when they work in an integrated fashion and are required to show the same kind of dynamic behavior. It requires high accuracy regulation of motion states such as the position, velocity and orientation. Therefore, the challenge lies in providing synchronized control strategy and real-time communications among the multi-composed systems.
Motion synchronization has been studied for a number of axes or motors in manufacturing process. A cross-coupling concept was proposed in early 1980s [1] where errors between systems (coupled errors) are to be synchronized. Since then, efforts of using this concept to improve synchronization performance of two-axis motions include the work of Kulkarni [2] and Kamano [3]. Other approaches include fuzzy logic coupling [4] and use of neuro-controllers [5]. Much of recent work can be found in the field of coordinated robots and multiple UAVs in formation flying, such as Jadbabaie [6], Beard [7], Angeles [8], and Lau [9].
With respect to flying vehicles, U.S. Pat. No. 6,271,768 to Frazier, Jr. et al. describes a system for avoiding traffic collisions, involving the cooperation of a follower aircraft in conjunction with lead aircraft. Data between the aircrafts are shared to coordinate flight.
Similarly, U.S. Pat. No. 6,718,236 issued to Hammer et al. teaches a method of trajectory planning and prediction among vehicles in their coordinated maneuver. The approach involves receiving and using the state data of numerous vehicles to coordinate them within a common maneuver. In this aspect, the approach is focused on predicting the trajectory states of the vehicles to conduct the coordinated maneuvers. However, what is not addressed is how to adjust to maintain such maneuvers, when coordination is slightly deviated and under disturbance. In other words, said prior art disclosure does not provide an automated control approach.
In general, a synchronized control approach using the cross-coupling strategy takes tracking actions by feeding back synchronization errors such as coupling errors. It creates interconnections that render mutual synchronization of involved multi-system motions. The strategy ensures stability and simultaneous convergence. However, there are still limitations in its implementation and operations.
First of all, there are many alternatives in selecting proper coupling errors for motion control. For example, one can compare its relative position with every other object in the system, or in some cases only the relative positions between the neighbouring systems are of interest. Obviously, different choices of coupling errors will lead to different synchronized control algorithms. Quite often it is difficult to predict which choice is better suited for a specific application in terms of dynamic performance before it is implemented and tested. However, changing the coupling error selections and the corresponding synchronizing control algorithms generally involves fundamental architecture modifications in the context of prior art solutions. It is a time-consuming and expensive task, and for some applications not financially viable. In these situations, the design suffers from performance degradation due to an early-stage selection mistake. Unfortunately, such a mistake is almost impossible to avoid upfront.
Secondly, one may argue that collecting all synchronization information possible among the systems will be more accurate and exhaustive, and will lead to better motion synchronization control. While this might be true in theory, in practice this will likely suffer from computational complexity that prevents the proposed controller from implementation. Given these realities, one needs to find a balance in trade-off between the synchronization strategies and their practical affordability.
In view of the foregoing, what is needed is a motion control method that overcomes limitations of prior art in terms of ad hoc selection of synchronization strategy, challenges in implementation modification, and trial-and-error evaluation of dynamic performance. In particular, what is needed is a uniform framework of motion synchronization.